python - Pandas: Exponentially decaying sum with variable weights

Question

Similar to this question Exponential Decay on Python Pandas DataFrame, I would like to quickly compute exponentially decaying sums for some columns in a data frame. However, the rows in the data frame are not evenly spaced in time. Hence while exponential_sum[i] = column_to_sum[i] + np.exp(-const*(time[i]-time[i-1])) * exponential_sum[i-1], the weight np.exp(...) does not factor out and it's not obvious to me how to change to that question and still take advantage of pandas/numpy vectorization. Is there a pandas vectorized solution to this problem?

To illustrate the desired calculation, here is a sample frame with the exponential moving sum of A stored in Sum using a decay constant of 1:

    time  A       Sum
0   1.00  1  1.000000
1   2.10  3  3.332871
2   2.13 -1  2.234370
3   3.70  7  7.464850
4  10.00  2  2.013708
5  10.20  1  2.648684

Answer 1

This question is more complicated than it first appeared. I ended up using numba's jit to compile a generator function to calculate the exponential sums. My end result calculates the exponential sum of 5 million rows in under a second on my computer, which hopefully is fast enough for your needs.

# Initial dataframe.
df = pd.DataFrame({'time': [1, 2.1, 2.13, 3.7, 10, 10.2], 
                   'A': [1, 3, -1, 7, 2, 1]})

# Initial decay parameter.
decay_constant = 1

We can define the decay weights as exp(-time_delta * decay_constant), and set its initial value equal to one:

df['weight'] = np.exp(-df.time.diff() * decay_constant)
df.weight.iat[0] = 1

>>> df
   A   time    weight
0  1   1.00  1.000000
1  3   2.10  0.332871
2 -1   2.13  0.970446
3  7   3.70  0.208045
4  2  10.00  0.001836
5  1  10.20  0.818731

Now we'll use jit from numba to optimize a generator function that calculates the exponential sums:

from numba import jit

@jit(nopython=True)
def exponential_sum(A, k):
    total = A[0]
    yield total
    for i in xrange(1, len(A)):  # Use range in Python 3.
        total = total * k[i] + A[i]
        yield total

We'll use the generator to add the values to the dataframe:

df['expSum'] = list(exponential_sum(df.A.values, df.weight.values))

Which produces the desired output:

>>> df
   A   time    weight    expSum
0  1   1.00  1.000000  1.000000
1  3   2.10  0.332871  3.332871
2 -1   2.13  0.970446  2.234370
3  7   3.70  0.208045  7.464850
4  2  10.00  0.001836  2.013708
5  1  10.20  0.818731  2.648684

So let's scale to 5 million rows and check performance:

df = pd.DataFrame({'time': np.random.rand(5e6).cumsum(), 'A': np.random.randint(1, 10, 5e6)})
df['weight'] = np.exp(-df.time.diff() * decay_constant)
df.weight.iat[0] = 1

%%timeit -n 10 
df['expSum'] = list(exponential_sum(df.A.values, df.weight.values))
10 loops, best of 3: 726 ms per loop